So who owns large stakes in OpenAI exactly? Seems like a somewhat important question given how important the company is shaping up to be to the future of society.
"The nonprofit, OpenAI Inc., is the sole controlling shareholder of OpenAI LP" from Wikipedia.
Clearly Microsoft has a large stake as well. It sounds like equity was distributed to employees, but according to Fortune some was sold: "OpenAI’s other investors include Hoffman’s charitable foundation and Khosla Ventures. Last year, Sequoia Capital, Tiger Global Management, Bedrock Capital, and Andreessen Horowitz reportedly purchased shares from preexisting shareholders in a sale valuing the company at around $20 billion, according to The Information."
So, the usual suspects, then. The more I learn about OpenAI, the more it looks like just another SV venture, not some special "for the good of humanity" thing.
Is there an example of a solely "for the good of humanity" thing that has come out of SV? Seems like a strange thing to expect from the technology sector. I think tech companies are (very) net positive for society but don't have to be designed as solely for the good of humanity to achieve this goal.
>Yahoo (RIP) was by reputation as community-minded and pro-open-source as a for-profit business citizen can get.
As someone who went through Yahoo acquisition and total bungling of tumblr, where community feedback fell on deaf ears, that's news to me. (Granted, the next owners were the ones who immediately managed to drop the userbase by like 80%, so maybe you're not totally wrong)
I said was. Meaning pre-2012. What year in your opinion did Yahoo jump the shark, as an employer?
(IIRC Yahoo culture had the reputation for being very friendly to employees open-sourcing their code, and not aggressively pursuing BS patent suits; this was different to most of MAANG + telecomms.)
Exactly the point I was making. We may quibble about whether or not SV companies overall have been a net positive, but that's neither here nor there.
OpenAI has been selling themselves as a do-gooder kind of project. I think that they're being disingenuous in doing so. They're building just a regular old SV money-spinner.
Just to piggyback on these guys not being the good guys, I don't think good people would have released this yet. This is gas on a fire when you look at the issues people are having figuring out whats going on in the world and making sense of it.
Every interaction with the public and these AIs I see screams "this was not ready for general consumption"
> I think tech companies are (very) net positive for society
There are certainly huge positives, but do you really feel something like Facebook is a net positive? Facebook, which intentionally stoke(d/s) genocide? Genocides have existed before Facebook, yes, but so did communication and racist relatives.
Do you have a source on them intentionally stoking genocide? I’m not fan of Facebook, but if there’s reliable evidence on that I’d expect summons to The Hague in short order, which I’ve yet to see.
Some people certainly believe so[1], there are also plenty of other links if searching for ’facebook myanmar genocide’ (though I would assume they a few common sources). But intentions are of course hard to prove.
Investors include Y Combinator, Reid Hoffman, Peter Thiel, Khosla Ventures, Sequoia Capital, Andreessen Horowitz, Microsoft, Amazon, Infosys, Tiger Global, Elevation Capital, Bedrock Capital, Wikus Ventures, Social Discovery Ventures, Pre IPO Club, Matthew Brown Companies, Change.org, and Fenrir.
This is like how they get a cut of each Android phone sold. Microsoft is gonna profit off of every new wave of technology that others built until the end of time, aren’t they
> company is shaping up to be to the future of society
It's definitely a terrifyingly powerful company, but "future of society" seems a little strong.
I think it's the future for spam, writing rote emails/documents/code, disinformation, plagiarism, lying, cheating, and impersonation, but those are subsets of society -- not all of society.
I've already had managers try to weigh in on technical discussions by posting ChatGPT's "thoughts" as if it had a seat at the table. They also quoted it to answer a question of when we need to worry about scale (a big part of the discussion we were having).
The answer was useless btw. Just a coarse, high-level, unactionable summary of what we had already talked about. Dressed up in a few nicely-worded paragraphs.
I wonder if this is gonna be a new pain in my ass. Wait until managers start using it to disagree with my estimates - I might blow my top lol.
What a bleak outlook you have. Here are some good things it can do:
1. Answer student questions and effectively act as a tutor. Duolingo is already using it like this, and ChatGPT can of course do it directly as well.
2. Make natural language interfaces to APIs easily. Look at the ChatGPT plugins announced yesterday for examples.
3. Provide basic customer support. Ideally, it could answer most common and basic questions and possibly even fix common problems via a plugin. Then the actual human customer support could step in for more complex problems.
Regarding 1., it appears to have about a 30% accuracy rate, and the other 60% is complete nonsense, often complete with fabricated citations. I dearly hope that nobody is ever encouraged to have this machine as their tutor.
30% accuracy rate in what exactly? Take a look at the GPT-4 announcement page for graphs showing the accuracy on different standardized tests. It’s not perfect, but making improvements with each release.
One big area where it does poorly right now is math. But they just announced a ChatGPT plugin for Wolfram, which I expect will make it very good at math. Wolfram also has a large database of curated information to draw on.
Technology improves over time. GPT is still new and improving quickly. What it does now isn’t perfect, but it is still incredible.
There's a post on /r/askhistorians where somebody asked ChatGPT for book recommendations on various historical topics. Some of them didn't exist. It actually took an expert reader to identify which books were made up, misatributed, and so on. That's much worse than nothing: it's a horrific timewaste.
My guess is stuff like math, where you can fairly easilly verify the factuality of ChatGPT's answers, is an area where you could certainly see progress. More general stuff like history, where it's important to have a really firm grasp of facts, inutition, and nuance, ChatGPT will likely be hard to improve, and worse, much harder to verify. Worse, these things can be insiduous: if you've learned something straightforwardly wrong, it corrupts future conclusions drawn from that erroneous premise.
I think the plugin system will ultimately help for most areas where LLMs are weak today.
Need to do math? Use the Wolfram plugin.
Need to have hard facts from reliable and citable sources? Use a plugin that queries databases like Arxiv. The LLM could give you links to sources and provide quotes from those sources to support its reasoning.
They might do, but what error rate do you think is acceptable? How do you actually measure and test the error rate? It's a use I can imagine in the future, but I think it's really premature to be using 'personal tutor' as a benefit (as openai do in their advertizing materials) when the program, as it stands, is essentially a fluent and convincing bullshitter, which is the single worst possible trait for a teacher.
Error is acceptable in all things that don't need to be deterministic.
Which is most thing in life. How do I discover a good career path? Why do you structure a repository of a program into folders? What is the best place to vacation? What is the best way to learn math? What is a good way to articulate socialism? How do I increase my vocabulary? What is corporate strategy?
Ask 10 different people "smart" people (define smart however you want), you'll get 10 different answers to these questions. These are all questions an LLM could answer amazingly. Probably a lot better than most humans.
If you don't ask it what 2+2 is or who came to in America in 1875 then you get useful things.
Asking a LLM deterministic questions right now is like asking a calculator what the meaning of life is. If you use the tool for something it's not good at you get unusable answers.
If you ask an idiot what he thinks about something, and he gives you a totally wrong answer, you have still learned at least one fact: a person believes a thing. As a human, living in a democracy, that has some worth. ChatGPT's wrong answer has absolutely no value at all.
Further, 10 different smart people will give 10 different answers because they have coherent worldviews and biases and proclivities, so by accounting for those, you can work out what the right answer is. Even if ChatGPT was anywhere close to a human expert when it comes to accuracy (what's the error rate in a peer reviewed journal article?) it would still have no coherent worldview or bias to contextualize its statements.
I see what you’re saying. The world has lost its collective mind.
HN seems to want to hand over the keys to the kingdom to basically a string generator. The string generator believes nothing, understands nothing, knows nothing but here we are.
Any intelligence that gpt4 shows is an emergent property. Humans are the ones reading GPT’s output and imputing meaning to it.
Reminds me of astrology and mass hysteria - people convincing each other to give this new oracle a chance because they personally have seen value in its ramblings.
I understand what you're saying. I just thing the world isn't so black and white. When you ask the idiot, or the smart person for that matter, a question, you have no idea if they are right or not. You only know after the fact when you get enough data to prove them wrong or someone that you trust more than that person tells you otherwise.
What is your error rate? What is my error rate? All of this stuff is unknown because we don't have counter factuals and we don't think of the world in this way (Did you order the correct food at dinner? Did you wash your clothes at the optimal time?)
To me you're thinking of it as a classical deterministic (binary) computer rather than a probabilistic thing. It's not an oracle, or a miracle, or anything other than some thing that gives useful information some percentage of the time. If something has to be right 100% of the time for it to be useful, or even 60% of the time, then the world is missing out on a lot of value.
Investors are right ~51% of the time, startup founders in the aggregate are right ~10% of the time, a great batting average is ~30%, etc.
The Hard Thing About Hard Things. We bought a copy for every employee at my last company to help convey the messages that startups are not designed to be tons of fun but rather a tough (yet rewarding) grind. This was especially important for people who were coming from bigger companies to understand.
It's a good book, but the part where he says to collude with your friends on hiring (no poaching, no cold calling, informing the other when someone applies, etc)... that's what Google/Adobe/Apple/etc were sued for a while back. Don't do that.
One measure of a high-quality C-level performer is the quality of their teams. The best C-level people can bring in very high-quality managers and individual contributors underneath them. Below average C-level people really struggle on this front.
Also, for some C-level roles there are good and objective ways to measure performance. For example, for a Chief Revenue Officer you have (obviously) revenue. There are a ton of confounding variables of course but in general CROs who consistently out-perform plan are better than those who consistently under-perform plan.
I've definitely seen CEOs with enormous amounts of success (in the companies they led previously) bring in dogshit teams to their current company. I've also seen dogshit CEOs bring in amazing people.
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Our mission is to help organizations build diverse, cohesive and better teams. We've been building a variety of hiring solutions to help companies like Tesla, Facebook and Lyft leverage the wealth of information online to find amazing prospective employees and make better hiring decisions. We also deeply care about promoting diversity in the tech world through our products and recently hired Leslie Miley, who'll be helping us push the industry to improve their diversity and inclusion practices.
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We're actively looking to grow the engineering team across the stack including:
Engineering Values: we greatly value creative, inquisitive and collaborative colleagues that care about helping to improve the recruiting world for the better. We have great work-life balance and benefits and have a tightly-knit engineering team that truly enjoys working together.
If you're interested, you can either check out our open positions at http://www.entelo.com/careers or you can email me directly at jon at entelo dot com (I am the founder, CEO).
Entelo - San Francisco (SOMA), CA - Full-time - http://www.entelo.com/ ; ONSITE only, no remote at the moment.
Our mission is to help organizations build diverse, cohesive and better teams. We've been building a variety of hiring solutions to help companies like Tesla, Zenefits and Lyft leverage the wealth of information online to find amazing prospective employees and make better hiring decisions. The sales and marketing worlds have many solutions that enable them to be more efficient and productive and it is our belief that we can adapt the best that sales and marketers have to recruiting/talent departments.
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If you're interested in any of our open positions, you can reach out to me directly (I'm the founder) at jon at entelo dot com
Entelo - San Francisco (SOMA), CA - Full-time - http://www.entelo.com/ ; ONSITE only, no remote at the moment.
Our mission is to help organizations build diverse, cohesive and better teams. We've been building a variety of hiring solutions to help companies like Facebook, Salesforce, Zenefits and Lyft leverage the wealth of information online to find amazing prospective employees and make better hiring decisions. The sales and marketing worlds have many solutions that enable them to be more efficient and productive and it is our belief that we can adapt the best that sales and marketers have to recruiting/talent departments.
We're currently looking for a variety of positions including:
-Sr. Backend Engineer/Architect: Our backend team deals with crawling, parsing, normalization and aggregation of hundreds of millions of social profiles (i.e. Linkedin, Github, Twitter) and use complex algorithmic and statistical methods to match disparate social profiles. We use a Java/Postgres architecture for the backend and have dabbled with using Go.
-Lead Data Engineer: We're looking for someone to help us continue to build out our analytics framework for prediction of when people are most likely to be looking for a new opportunity. We use Amazon Redshift for data warehousing but are open to those that have extensive experience with "big data" stacks (Hadoop, Spark, Storm, Kafka, etc.)
-DevOps Engineer: We're looking for our first dedicated engineer to own DevOps for the long term. We take a software-centric (as opposed to an ops-centric) approach to DevOps, and have every engineer setup on PagerDuty in case something breaks in the middle of the night (hopefully we'll avoid that!). We use a variety of modern technologies including Docker, AWS, Rails, ElasticSearch, Kubernetes and other tools such as New Relic, Loggly and PagerDuty.
If you're interested in any of our open positions, you can reach out to me directly (I'm the founder) at jon at entelo dot com
Entelo - San Francisco (SOMA), CA - Full-time - http://www.entelo.com/ ; ONSITE only, no remote at the moment.
Our mission is to help organizations build diverse, cohesive and better teams. We've been building a variety of hiring solutions to help companies like Facebook, Salesforce, Zenefits and Lyft leverage the wealth of information online to find amazing prospective employees and make better hiring decisions. The sales and marketing worlds have many solutions that enable them to be more efficient and productive and it is our belief that we can adapt the best that sales and marketers have to recruiting/talent departments.
We're currently looking for a variety of positions including:
-Sr. Backend Engineer/Architect: Our backend team deals with crawling, parsing and normalization of hundreds of millions of social profiles (i.e. Linkedin, Github, Twitter) and use complex algorithmic and statistical methods to match disparate social profiles. We have used a combination of Scala/MongoDB/RabbitMQ but have been looking towards using Go/Postgres/Java on the backend instead.
-Lead Data Engineer: We're looking for someone to help us continue to build out our analytics framework for prediction of when people are most likely to be looking for a new opportunity. We use Amazon Redshift for data warehousing but are open to those that have extensive experience with big data stacks (Hadoop, Spark, Storm, Kafka, etc.)
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